Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gr...

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Vydané v:	Medical & biological engineering & computing Ročník 60; číslo 3; s. 663 - 681
Hlavní autori:	Deng, Xiongshi, Li, Min, Deng, Shaobo, Wang, Lei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022 Springer Nature B.V
Predmet:	Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Cancer Classification Computer Applications Datasets DNA microarrays Feature selection Gene expression Genes Genetic algorithms Human Physiology Humans Imaging Microarray Analysis Multiple objective analysis Neoplasms - genetics Optimization Original Article Radiology Multi-objective genetic algorithm Microarray gene expression Feature selection XGBoost Classification
ISSN:	0140-0118, 1741-0444, 1741-0444
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